TERRA project teams will integrate the agriculture, information technology, and engineering communities to design and apply new tools to the development of improved varieties of energy sorghum, a crop used to produce biofuel. Producing the large amounts of biomass needed for biofuels to displace petroleum requires significant improvements to the productivity and efficiency of biofuel crops. The teams will enhance methods for crop phenotyping (identifying and measuring the physical characteristics of plants), which are currently time-intensive and imprecise. The new approaches will include automated methods for observing and recording characteristics of plants and advanced algorithms for analyzing data and predicting plant growth potential. These innovations will accelerate the annual yield gains of traditional plant breeding and support the discovery of new crop traits that improve water productivity and nutrient use efficiency.

Project Innovation + Advantages:

The University of Illinois Urbana-Champaign (UIUC) with partners, Cornell University and Signetron Inc., will develop a small semi-autonomous, ground-based vehicle called TERRA-MEPP (Mobile Energy-Crop Phenotyping Platform). The platform performs high-throughput field-based data collection for bioenergy crops, providing on-the-go measurements of the physical structure of individual plants. TERRA-MEPP will use visual, thermal, and multi-spectral sensors to collect data and create 3-D reconstructions of individual plants. Newly developed software will interpret the data and a model-based data synthesis system will enable breeders to select the most promising sorghum lines for bioenergy production much sooner than currently possible, dramatically increasing the rate of genetic advancements in biomass.

Potential Impact:

If successful, UIUC will use its TERRA-MEPP system to make in-field high-throughput data collection accessible to breeders and other scientists at a price far below currently available options. The increased speed of data collection and analysis may lead to significant crop improvements.

Security:

Improved biofuel crops could lead to increased production of domestic biofuels, reducing dependence on foreign sources of transportation fuels.

Environment:

Increased use of biofuels could significantly reduce CO2 emissions from transportation, and improved varieties of biofuel crops could use less water and be more resistant to environmental stress.

Economy:

The UIUC team estimates that the TERRA-MEPP platform could be deployed at an economical price point that could encourage adoption of this state-of-the art phenotyping platform by small scale breeders and can help improve bioenergy crop varieties.

Innovation Update:

(As of May 2018)UIUC has developed new platforms and tools to help improve the quality, quantity, and versatility of phenotypic data for trait discovery in sorghum. The team built two robots, TerraMepp and TerraSentia, and associated analytics software to collect and analyze phenotypic data. UIUC used this data to build a biophysical model able to predict end-of-season performance of individual plants based on early season observations. The team developed algorithms that can identify individual stems and estimate stem width and plant height with over 80% accuracy. To identify genes underlying specific traits, the team constructed a high-throughput sequence data analysis pipeline for whole-genome resequencing. High quality genetic markers are essential to enable molecular breeding in biomass sorghum because they allow researchers to determine the best crosses to make promising varieties and then select the plants with the highest yield potential early in the season.

Potential commercial products include the autonomous robots and novel plant data algorithms, which could be marketed individually or in a variety of packages. Through an early adopter program, the team’s TerraSentia robot is already available to industry, academia, and other organizations for $5,000.

For a detailed assessment of the UIUC project and impact, please click here.